A research and education initiative at the MIT
Sloan School of Management
Consumer Surplus in the Digital Economy:
Estimating the Value of Increased Product Variety at
Online Booksellers
Paper 176
Erik Brynjolfsson
Michael D. Smith
Yu (Jeffrey) Hu
November 2003
For more information,
please visit our website at http://ebusiness.mit.edu
or contact the Center directly at ebusiness@mit.edu
or 617-253-7054
Consumer Surplus in the Digital Economy:
Estimating the Value of Increased Product
Variety at Online Booksellers
Erik Brynjolfsson • Yu (Jeffrey) Hu • Michael D. Smith
Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
The Heinz School of Public Policy and Management, Carnegie Mellon University,
Pittsburgh, Pennsylvania 15213
erikb@mit.edu • yuhu@mit.edu • mds@cmu.edu
W
e present a framework and empirical estimates that quantify the economic impact of
increased product variety made available through electronic markets. While efficiency
gains from increased competition significantly enhance consumer surplus, for instance, by
leading to lower average selling prices, our present research shows that increased product
variety made available through electronic markets can be a significantly larger source of
consumer surplus gains.
One reason for increased product variety on the Internet is the ability of online retailers
to catalog, recommend, and provide a large number of products for sale. For example, the
number of book titles available at Amazon.com is more than 23 times larger than the number
of books on the shelves of a typical Barnes & Noble superstore, and 57 times greater than
the number of books stocked in a typical large independent bookstore.
Our analysis indicates that the increased product variety of online bookstores enhanced
consumer welfare by $731 million to $1.03 billion in the year 2000, which is between 7 and 10
times as large as the consumer welfare gain from increased competition and lower prices in
this market. There may also be large welfare gains in other SKU-intensive consumer goods
such as music, movies, consumer electronics, and computer software and hardware.
(Consumer Surplus; Product Variety; Electronic Commerce; Welfare; Internet)
1.
Introduction
Clearly, new goods are at the heart of economic
progress. But that realization is only the beginning of
an understanding of the economics of new goods. The
value created by new goods must somehow be converted into an exact quantitative measure (Timothy F. Bresnahan and Robert J. Gordon 1997, p. 1)
The Internet is providing access for people who just
can’t find the book they are looking for in a store.
(Nora Rawlinson, editor of Publishers Weekly, quoted
Lyster 1999, p. A6.)
Information technology (IT) facilitates the delivery
of many new products and services across electronic
Management Science © 2003 INFORMS
Vol. 49, No. 11, November 2003, pp. 1580–1596
networks. As these electronic networks develop and
mature, it will be important to quantify their value
for customers, merchants, shareholders, and society.
While much of the attention in academic research and
in the press has been on the relative operational efficiency of the online channel versus traditional channels, we believe that important benefits lie in new
products and services made available through these
channels. While it has been relatively easy to quantify
the operational costs of each channel, the value of
new products and services made available through
electronic networks has remained unquantified. By
0025-1909/03/4911/1580
1526-5501 electronic ISSN
BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
default, this value has been ignored, effectively treating convenience and selection as if its value were zero.
Our research focuses on increased product variety, which is one category of new products and services made available through electronic networks.
Internet retailers have nearly unlimited “virtual
inventory” through centralized warehouses and dropshipping agreements with distributors (e.g., Bianco
1997, Mendelson and Meza 2002). Because of this,
they can offer convenient access to a larger selection
of products than can brick-and-mortar retailers.
Table 1 shows the difference between the number
of items available at Amazon.com and a typical
large brick-and-mortar retailer for several consumer
product categories.1 For example, Amazon.com and
barnesandnoble.com provide easy access to each of
the 2.3 million books in print (and millions more
used and out-of-print titles) while conventional brickand-mortar stores carry between 40,000 and 100,000
unique titles on their shelves. Thus, online consumers
are easily able to locate, evaluate, order, and receive
millions of books that are not available on the shelves
of local bookstores. Large differences in product variety are also seen in music, movies, and consumer electronics products. Even Wal-Mart Supercenters, which
range in size from 109,000 to 230,000 square feet, only
carry one-sixth of the number of SKUs that are carried
by Walmart.com (Owen 2002).
While some of these products may be available
from specialty stores or special ordered through
brick-and-mortar stores, the search and transaction
costs to locate specialty stores or place special
orders are prohibitive for most consumers.2 In addition, the enhanced search features and personalized
1
Inventory values for Amazon.com were obtained from discussions
with industry executives, industry estimates and Bowker’s Books in
Print database (books), wholesale suppliers to Amazon.com (CDs),
and direct counting of normally stocked items. Inventory values
for brick-and-mortar stores were obtained from interviews with
managers and direct observation of inventory for Barnes & Noble
superstores (Books, CDs, DVDs), Best Buy (CDs, DVDs, digital
cameras, portable MP3 players, flatbed scanners), and CompUSA
(digital cameras, portable MP3 players, flatbed scanners).
2
To illustrate this difference, on November 26, 2001, one of the
authors ordered the same book through barnesandnoble.com and
through a special order at a local Barnes & Noble superstore. The
barnesandnoble.com order process took 3 minutes to place, arrived
Management Science/Vol. 49, No. 11, November 2003
Table 1
Product Variety Comparison for Internet and Brick-and-Mortar
Channels
Product category
Books
CDs
DVDs
Digital cameras
Portable MP3 players
Flatbed scanners
Amazon.com
Typical large
brick-and-mortar stores
2300000
250000
18000
213
128
171
40,000–100,000
5,000–15,000
500–1,500
36
16
13
recommendation tools offered by Internet retailers
allow consumers to locate products that would have
remained undiscovered in brick-and-mortar stores.
For instance, Amazon uses at least seven separate
“recommender systems” to help advise customers on
their purchases. Via these systems, new products,
including obscure books, are brought to the attention
of shoppers visiting their site or e-mailed as suggestions to past shoppers. Recommender systems have
the potential to automate “word of mouth,” speeding
the discovery and diffusion of new goods (Resnick
and Varian 1997).
In effect, the emergence of online retailers places a
specialty store and a personalized shopping assistant
at every shopper’s desktop. This improves the welfare of these consumers by allowing them to locate
and buy specialty products they otherwise would not
have purchased due to high transaction costs or low
product awareness. This effect will be especially beneficial to those consumers who live in remote areas
that do not have specialty retailers.
As one might expect, the lower transactions costs
offered by the Internet have led to increased orders
for many titles not previously stocked in brick-andmortar stores. Frank Urbanowski, Director of MIT
in 3 days, and cost $31.99. The Barnes & Noble order took nearly
1 hour to place, took 8 days to arrive, and cost $37.45. The store
was located 4.6 miles away from the author’s house (note that
Brynjolfsson and Smith 2000 found that the average person in the
United States lives 5.4 miles away from the closest bookstore). The
time to place the order included 21 minutes of driving time (roundtrip) to place the order; 8 minutes to park, search for the book in
the store, search for a sales person, and place the order; 20 minutes
of driving time to pick up the order; and 9 minutes to park and
pay for the special order.
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BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
Press, attributed the 12% increase in sales of backlist
titles directly to increased accessibility to these titles
through the Internet (Professional Publishing Report
1999). Similarly, Nora Rawlinson, the editor of Publishers Weekly, observes:
Publishers are finding that books on their backlists
are suddenly selling well. Bookstores are great for
browsing but they are difficult places to find a specific
title The Internet is providing access for people
who just can’t find the book they are looking for in a
store. (Lyster 1999, p. A6)
The differences in variety reflect underlying differences in the technology and economics of conventional and Internet retailers. As noted by Saul Hansell
in the New York Times:
The average book may sit on the shelf of a store for
six months or a year before it is bought. The cost
of this inventory in a chain of hundreds of stores is
huge. Amazon can keep just one or two copies in its
warehouse—and still make the title available to the
whole country—and restock as quickly as customers
buy books. (Hansell 2002, p. C2)
Further, anecdotal evidence suggests that consumers place a high value on the convenience offered
by Internet retailers when locating and purchasing
obscure products. For example, Yahoo/ACNielsen’s
2001 Internet Confidence Index lists “wide selection
of products” as one of the top three drivers of consumer e-commerce based on a survey of Internet purchasers. However, no systematic estimates exist to
empirically quantify the dollar value consumers place
on the increased product variety available through
Internet markets.3
This paper represents a first effort to apply
a methodology for estimating this value to one
prominent category of products offered by Internet
retailers—obscure book titles. Our methodology uses
a small set of generally available statistics that track
how consumers “vote with their dollars” and, thus,
may find application in a variety of product categories. The resulting estimates of consumer surplus will have important economic and public policy
3
Israilevich (2001) uses a theoretical model of product differentiation to calibrate how lower fixed costs of selling books on the Internet may have led to increased variety, and estimates the welfare
implications of it.
1582
implications, especially as investors and managers try
to understand and evaluate the value proposition of
Internet-based commerce.
The remainder of this paper proceeds as follows.
Section 2 presents the economic literature pertaining to consumer welfare gains from new goods
and increased product variety. Section 3 develops
a methodology to measure consumer welfare from
increased product variety offered in Internet markets.
Section 4 applies this methodology to obscure book
sales over the Internet and §5 concludes with some
broader implications. The Appendix summarizes both
the model development (§3) and the data necessary
to calibrate the model in a general market environment (§4).
2.
Literature Review
The development of an empirical methodology to estimate the welfare change resulting from price changes
can be traced to Hicks’ (1942) compensating variation measure. Historically, compensating variation has
been difficult to measure because it involves integration of the unobservable Hicksian compensated
demand curve. However, Hausman (1981) develops
a closed-form solution for measuring compensating
variation under standard linear or log-linear demand
functions. More recently, Hausman (1997a) shows that
the welfare effect of the introduction of a new product is equivalent to the welfare effect of a price drop
from the product’s “virtual price,” the price that sets
its demand to zero, to its current price. Applying this
technique, Hausman (1997a) estimates that the Federal Communications Commission’s decision to delay
the introduction of two telecommunication services
has reduced U.S. consumer welfare by billions of dollars a year. Subsequently, researchers have examined
the welfare effects of other new products in traditional
markets, using similar or more refined models. Examples include Hausman (1997b), Nevo (2001), Goolsbee
and Petrin (2001), Petrin (2001), and Hausman and
Leonard (2001). In addition, Bresnahan (1986) and
Brynjolfsson (1995) have looked at welfare gains from
IT investments.
Researchers in the field of macroeconomics have
also started to pay attention to new products or new
Management Science/Vol. 49, No. 11, November 2003
BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
varieties of products. Bils and Klenow (2001) find
that consumer spending has shifted away from products that have shown little variety gain. The Stigler
Commission (National Bureau of Economic Research
1961) and the Boskin Commission (Boskin Commission Report 1996) conclude that the greatest flaw in
the Consumer Price Index is its failure to adequately
account for new goods and quality improvements in
existing goods.
It is also worth noting that there is a large body
of marketing literature examining the relationship
between perceived variety and actual assortment.
Most researchers agree that consumers generally prefer more variety when given a choice (e.g., Baumol and Ide 1956, Kahn and Lehmann 1991). More
recently, researchers have shown that consumers’ perception of variety is influenced not only by the number of distinct products, but also by the repetition
frequency, organization of the display, and attribute
differences (e.g., Dreze et al. 1994, Broniarczyk et al.
1998, Hoch et al. 1999, van Herpen and Pieters 2002).
In this paper, we focus on the impact that increased
availability of products in the online channel has
on consumers’ actual purchase behavior. Thus, questions of shelf space and consumer perceptions are
muted relative to the actual assortment of products
and observed consumer behavior.
3.
Methodology
This paper applies and extends existing welfare estimation techniques to measure the consumer welfare
gain from the increased product variety made available through electronic markets. To do this, we define
the total effect of the introduction of new products
in online markets on consumer welfare as the difference in the consumer’s expenditure function before
and after the introduction, measured at the level of
postintroduction utility:
CV = epe0 pn0 u1 − epe1 pn1 u1 (1)
where pe0 and pe1 are the vectors of pre- and postintroduction prices of existing products, respectively, pn0 is
the virtual price of the new product (the price that sets
demand to zero), pn1 is the postintroduction price of
Management Science/Vol. 49, No. 11, November 2003
the new product, and u1 is the postintroduction utility level. In effect, Equation (1) measures how much
a pre-Internet consumer would need to be compensated to be just as well off as he or she would be after
the emergence of online markets.
We then follow Hausman and Leonard’s (2002)
derivation to break the total effect into the variety
effect resulting from the availability of the new product and the price effect resulting from changes of
prices of existing products:
CV = epe1 pn0 u1 − epe1 pn1 u1 + epe0 pn0 u1 − epe1 pn0 u1 (2)
When the vector of prices of existing products does
not change before and after the introduction of the
new product, i.e., pe0 = pe1 = pe , one only needs to
measure the variety effect, and we can redefine the
expenditure function such that epe · · ≡ e · · :
CV = epe pn0 u1 − epe pn1 u1 = e pn0 u1 − e pn1 u1 (3)
The assumption that pe0 = pe1 = pe appears to be
valid in our empirical context because the overwhelming majority of book prices charged by brickand-mortar stores have not changed as a result of the
emergence of online markets. Nearly all brick-andmortar stores sold most titles at the manufacturer’s
suggested list price before the emergence of online
markets and continue to do so today. Moreover, most
studies have shown that, if anything, Internet retailers tend to increase competition and place downward
pricing pressure on brick-and-mortar retailers (e.g.,
Brynjolfsson and Smith 2000, Scott Morton et al. 2001,
Brown and Goolsbee 2002, Baye et al. 2003). Thus,
if brick-and-mortar prices were to change at all, we
would expect them to decline. Our calculations under
the zero price change assumption would, therefore,
underestimate true gains in consumer surplus.
To apply Equation (3) in practice, we specify a standard log-linear demand function for the new product
made available by the Internet,
xp y
= Ap y (4)
where p is the price of the new product, y is
the income, is the price elasticity, and is the
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BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
income elasticity. This specification is the most widely
used specification in the literature of demand estimation and it well fits a wide variety of data (e.g.,
Brynjolfsson 1995; Hausman 1997a, b; Hausman and
Leonard 2002).4
Following Hausman (1981), we can use Roy’s identity to write Equation (4) as
xp y
= −
vp y
/p
vp y
/y
(5)
where vp y
is the indirect utility function. Solving
this partial differential equation gives
y 1−
p1+
+
1+ 1−
and the expenditure function
Ap1+ 1/1−
ep u
= 1 − u +
1+
vp y
= −A
(6)
(7)
Using Equations (3) and (7), it can be shown
(Hausman 1981) that the welfare impact of the introduction of a new product is given by
1/1−
1 − −
y pn0 x0 − pn1 x1 + y 1−
− y (8)
CV =
1+
where CV is the compensating variation, is the
income elasticity estimate, is the price elasticity,
y is income, (pn1 x1 are the postintroduction price
and quantity of the new product, and (pn0 x0 are the
preintroduction virtual price and quantity of the new
product.
Prior research has shown that income elasticity
effects can be ignored for typical consumer products where purchases are a small fraction of the
consumer’s annual income (e.g., Hausman 1997a,
Brynjolfsson 1995). Applying this assumption, i.e.,
= 0, Equation (8) simplifies to
p x
(9)
CV = − n1 1 1+
because the preintroduction quantity is zero and
pn0 x0 = 0. If income elasticity were positive, as is likely
4
The final result of welfare estimates will depend on the adopted
specification of demand function. However, earlier research (e.g.,
Brynjolfsson 1995, Hausman and Newey 1995) finds that using a
nonparametric specification with complete freedom to fit the data
may not significantly improve the accuracy of welfare estimates
over estimates using a standard log-linear specification.
1584
for books, including income elasticity in our calculations would increase our consumer surplus estimates
by a small amount (Varian 1992).
4.
Data and Results
We use this methodology to measure the consumer
surplus gain in Internet markets from access to books
not readily available through brick-and-mortar retailers. As noted above, for many consumers, these
obscure books can be properly categorized as “new”
products because, while they are readily available
in Internet markets, the transactions costs necessary
to acquire these goods in physical markets are prohibitively high. The availability of these books to
Internet consumers reflects, in part, the increased
inventory carrying capacity of Internet retailers.
Furthermore, recommendation lists, customer and
industry reviews, images of the book jacket and
selected book pages, and convenient search facilities allow Internet consumers to discover and
evaluate obscure books that would likely have
remained undiscovered in conventional retail environments where these books would be unavailable for
browsing.
This product category also provides a useful starting point for surplus measurement because it represents a relatively mature Internet market, and because
prior research has already measured the reduction
in prices from increased competition on the Internet
(e.g., Brynjolfsson and Smith 2000), providing a point
of reference for our surplus measurements.
In the following sections, we discuss how we estimate the parameters necessary to calculate the consumer surplus resulting from increased accessibility
to obscure books on the Internet: the price elasticity of
demand and the price and quantity of sales of obscure
books on the Internet.
4.1. Elasticity of Demand
The most straightforward approach to calculate elasticity of aggregate demand would be through direct
empirical estimation. In the conclusion section, we
discuss how elasticity of demand might be obtained
by partnering with a book publisher or with a retailer
with dominant market share to conduct a direct
Management Science/Vol. 49, No. 11, November 2003
BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
pricing experiment. Unfortunately, we were unable to
obtain cooperation from either publishers or retailers
to conduct such an experiment. In the absence of this
data, we estimate the elasticity of aggregate demand
by taking advantage of the characteristics of the book
industry structure and available industry statistics on
gross margins.
To do this, we first note that the book industry is
vertically structured as shown in Figure 1, where c is
the marginal cost of a book and pwi qwi pri , and qri
are wholesale price and quantity and retail price and
quantity for retailer i i = 1 2 N , respectively.5
Publishers set both list prices and wholesale prices of
the books they publish. They sell books to retailers,
either directly or through distributors, at prices that
are a set percentage of the books’ list prices, typically
between 43%–51% off list prices. Thus, a change in
the list price of a book would also result in a proportional change in the wholesale price of the book.6 Further, wholesale prices charged on an individual book
are almost the same across retailers, regardless of the
channel that the retailer operates in or the size of the
retailer (e.g., Clay et al. 2001). Thus, we have pwi = pw
for i = 1 2 N .
Retailer i i = 1 2 N receives books from
either publishers or distributors, and then sells these
books to consumers at some discount off list price.
Books in different categories are sold at different preset discounts off their list prices. For a particular book,
the discount off its list price will not change as a result
of a change in its list price.7 Most obscure books are
5
This structure is accurate for the vast majority of consumer purchases, which are made through bookstores. However, in a few
cases, customers choose to special order books directly from the
publisher. For our purposes, as long as these books are available
from the publisher, at the same prices both before and after Internet
book retailers introduce the increased selection of books, publisher
special orders will not affect our underlying model. This follows
because, as noted above, the unchanged prices of existing products
are irrelevant to consumer surplus calculations from the introduction of new products.
6
Source: Vicki Jennings, MIT Press, July 23, 2002, conversation.
7
Moreover, this discount off list price is usually set by retailers in
multiples of 10%. For example, in a representative sample of 23,744
titles sold at Amazon.com in late 1999, 88.5% of them follow such a
pricing pattern—29.5% have 0% discount; 1.4% have 10% discount;
Management Science/Vol. 49, No. 11, November 2003
sold at their full list prices.8 Therefore, for a given
obscure title, there exists a stable relationship between
the book’s wholesale price and retailer i’s retail price,
pw = ki pri , where ki is a constant between 0 and 1.
In addition, we assume that qwi = qri . This holds for
two reasons. First, most obscure books are ordered
by retailers from publishers and wholesalers after a
customer initiates a purchase. Second, according to
several publishers we interviewed and the American
Wholesale Booksellers Association, the vast majority
of books are sold on consignment. Typically, retailers can return unsold or returned books to publishers or distributors without penalty (except for return
shipping). Given this, if we define qr ≡ Ni=1 qri as the
total quantity sold by retailers to consumers and qw ≡
N
i=1 qwi as the total net quantity sold by the book’s
publisher to retailers, we easily get qr = qw .
If we define retailer i’s market share on this book as
si ≡ qri /qr , then the weighted retail market price can
be written as
p̄ ≡
N
i=1
si pri =
N
si
pw k
i=1 i
One can show that the elasticity of aggregate
demand in the retailing market equals the elasticity
of demand faced by the publisher:9
N
p̄ dqr
p̄ dqw
i=1 si /ki pw dqw
=
= qr d p̄
qw d p̄ qw Ni=1 si /ki dpw
N
p dq
i=1 si /ki pw dqw
=
= w w
(10)
N
qw dpw
qw i=1 si /ki dpw
34.3% have 20% discount; 18.4% have 30% discount; 1.6% have 40%
discount; 3% have 50% discount; and 0.1% have 60, 70, 80, or 90%
discounts. See Smith (2001) for more information on this sample of
titles.
8
We selected 100 books at random from a sample of all customer
searches at DealTime (dealtime.com) on July 2, 2001. Among the
37 books with Amazon.com sales ranks greater than 100,000, 86%
are sold at their respective list prices at Amazon.com (versus 41%
for the remaining titles). Lee and Png (2002) also collect data showing that bookstores typically offer zero discounts on nonbest-seller
titles.
9
The elasticity of aggregate demand can be thought of as the
percentage change in total market sales if all retailers changed
their price by one percentage point. In general, this will be less
than the elasticity of demand faced by a particular retailer acting
independently.
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BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
Figure 1
Vertical Industry Structure in Book Sales
pr1 , qr1
Retailer 1
pw1 , qw1
pr2 , qr2
Retailer 2
c
pw2 , qw2
Publisher
.
.
.
pwN , qwN
prN , qrN
Retailer N
Because the publisher of a particular title has total
control over establishing the title’s list and wholesale price, it is reasonable to apply the well-known
Lerner index formula to estimate the price elasticity
of demand faced by the publisher:10
pi − Ci
1
=− pi
ii
(11)
Publishers sell books to both online retailers and
brick-and-mortar retailers, either directly or through
distributors, at wholesale prices that are a set percentage of books’ list price, typically between 43%–51%
off list prices. Publishers incur the same production
costs whether books are sold to an online retailer
or to a brick-and-mortar retailer. Therefore, publishers sell obscure books to online retailers and brick10
This form of the Lerner index is applicable to single product
monopolists, multiproduct monopolists who maximize profits on a
per product basis, or in instances where crosselasticity is zero. In
the more general multiproduct monopolist case, the Lerner index
for product i is given by
and-mortar retailers at the same gross margins. Discussions with various publishers indicate that the
gross margin of a typical obscure title is between
56%–64%.11 Thus, using (11), the elasticity of demand
faced by the publisher is between −156 and −179,
and by (10), this is also the aggregate demand in the
retailing market.
This estimate can also be compared with what other
researchers have obtained, albeit using retailer data.
For example, Brynjolfsson et al. (2002) use shopbot
data to calculate an own-price elasticity of −147
for retailers listing their prices at a popular shopbot, which is somewhat lower in absolute value
than our estimates. Similarly, Chevalier and Goolsbee
(2003) estimate a demand system for two online book
retailers: Amazon.com and barnesandnoble.com. The
imputed demand elasticity using their calculations is
also lower than our elasticity estimate. As noted in (9),
a smaller elasticity will translate to a larger consumer
surplus estimate.
pj − Cj qj ji
pi − Ci
1
=−
−
pi
ii j=i
pi qi ii
where i and j are indexes for products. However, in the Internet
book market, all available evidence suggests that prices are set on
an individual book basis and, thus, we use estimates based on
Equation (11) for our elasticity calculations. If the publisher is not
a monopolist for the book title being sold, then this formula will
overestimate the true price elasticity of demand (in absolute value)
and underestimate true consumer surplus.
1586
11
For example, data from the American Association of Publishers
suggest that the gross margin of a typical book is between 56%–
58% depending upon whether shipping is included. A typical MIT
Press book has a gross margin of approximately 63% (Source: Vicki
Jennings, MIT Press, conversation). A large publisher of technical books revealed that each of their books had gross margins of
between 58%–64% during the past several years. A large publisher
of trade books revealed that each of their books had gross margins
of approximately 60%.
Management Science/Vol. 49, No. 11, November 2003
BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
4.2. Sales of Obscure Titles on the Internet
Internet retailers are extremely hesitant about releasing specific sales data, and we were unable to obtain
sales data from a major Internet retailer that would
allow us to estimate the sales of obscure titles on
the Internet. However, we were able to obtain data
from one publisher that allowed us to estimate the
proportion of sales of obscure titles in total sales
at Amazon.com. This proportion should generalize to the overall Internet book market, given that
Amazon.com has approximately a 70% share of the
Internet book market (Ehrens and Markus 2000).
This publisher provided data matching the publisher’s weekly sales for 321 titles to the sales rank
observed at Amazon.com’s website during the same
week. According to Amazon.com’s frequently asked
questions page:
The [rank] calculation is based on Amazon.com sales
and is updated regularly. The top 10,000 best sellers
are updated each hour to reflect sales in the preceding
24 hours. The next 100,000 are updated daily.12
These data, gathered for three weeks in the summer
of 2001, provide a fairly robust basis for correlating
sales and sales rank at Amazon.com. The observed
weekly sales range from 1 to 481 units sold and the
observed weekly sales rank ranges from the 238 to
961,367. Summary statistics for this data are shown in
Table 2.13
We fit our data on sales and sales rank to a loglinear distribution:
log Quantity
= 1 + 2 · log Rank
+ (12)
where
is orthogonal to log(Rank) and is spherical, following the standard ordinary least squares
assumptions.
This approach was suggested by Madeline Schnapp
of O’Reilly Books who reported excellent success estimating competitors’ unit books sales by comparing
their books’ sales ranks to O’Reilly’s. Chevalier and
12
Available at http://www.amazon.com/exec/obidos/tg/browse/
-/525376/104-2977251-9307125. Further experimentation demonstrated that the sales rank does not include used book sales or sales
through Amazon’s marketplace sellers.
13
The panel of titles changed somewhat during the sample period
and, as a result, not all titles were tracked in all weeks.
Management Science/Vol. 49, No. 11, November 2003
Table 2
Summary Statistics for Amazon Sales Rank Data
Variable
Obs.
Mean
S.D.
Min
Max
Weekly sales
Weekly sales rank
861
861
1917
3153285
3063
5835092
1
238
481
961367
Goolsbee (2003) also fit sales and sales rank data to
a (slightly different) log-linear distribution with good
success. Earlier applications include Pareto (1896),
who found that income can be approximated by such
a log-linear distribution, and Zipf (1949) who suggested that city size also follows a log-linear distribution with a slope of −1.
Regressing log(Quantity) onto a constant and
log(Rank), we obtain an estimate of 10.526 for 1 and
−0871 for 2 as shown in Table 3.14
The coefficients in this regression are highly significant and the R2 value suggests that our model is
precisely estimated. Furthermore, the estimates lead
to plausible sales rank results. Given our estimates,
a book with a rank of 10 is estimated to get 5,000
sales per week and a book with a rank of 100,000 gets,
on average, 1.6 sales per week. Likewise, integrating
under the curve for titles with rank from 1 to 2.3 million suggests that Amazon.com was selling books at
a rate of 99.4 million per year in the summer of 2001.
This estimate compares well with industry statistics.15
These estimates also favorably compare with Pareto
slope parameter estimates obtained by Chevalier and
Goolsbee (2003), using a clever and easily executed
experiment. To conduct this experiment, they first
obtained information from a publisher on a book with
14
A graphical analysis suggests that the size of the residuals
increases in rank, and a Breusch-Pagan test confirms the presence
of heteroskedasticity in the residuals. Thus, we use White heteroskedasticity consistent estimator (see Greene 2000, p. 463) to
estimate both coefficients. We also performed a test for structural
change by interacting log(Rank) with a dummy variable that took
on the value of one for ranks larger than 40,000. The coefficient on
this variable was positive (but statistically insignificant), suggesting that our results would, if anything, be strengthened if we based
our 2 upon only high-rank books.
15
Book Industry Trends 2001 (Book Industry Study Group 2001) lists
2000 total unit sales of books at 2.5 billion and their study also
shows that the Internet makes up 6% of total book purchases.
Amazon.com has a 70% share of the Internet book market (Ehrens
and Markus 2000).
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Table 3
Regression Results Amazon
Sales Rank Data
Variable
Coefficient
Constant
10526
0156
−0871
0017
08008
Log(Rank)
R2
relatively constant weekly sales, then purchased six
copies of the book in a 10-minute period, and tracked
the Amazon rank before and shortly after the purchases. Using the sales and sales rank before and after
the experiment, they estimated 2 as −0855 (note that
the " reported by Chevalier and Goolsbee 2003 corresponds to −1/2 ). They also estimated 2 from similar sales rank data reported by Weingarten (2001) and
Poynter (2000) as −0952 and −0834, respectively. We
performed a similar purchase experiment in September 2002 and calculated 2 as −0916.16 It is significant
that, while these parameter estimates rely on only 2
points, they are remarkably similar to the results calculated in Table 3, which are based on more than 800
points.
We can use the Pareto slope parameter estimate
from our data to calculate the proportion of unit sales
at Amazon that fall above a particular rank as
N
1 t 2 dt
N 2 +1
− x2 +1
rx N = xN
=
(13)
N 2 +1
− 1
1 t 2 dt
1
where x is the rank, and N is the total number of
available books.
What rank cutoff is appropriate for our purposes?
As noted above, we wish to estimate the gain in consumer surplus from access to books on the Internet
16
We selected a book whose Amazon.com rank on September 13,
2002 was 606,439. We checked the rank of this book each day
between September 14 and September 30 and noted 3 changes: on
Monday, September 16 the book jumped from 606,439 to 596,625; on
Monday, September 23 the book dropped from 596,625 to 599,352;
and on Monday, September 30 the book dropped from 599,352 to
601,457. We infer from this that Amazon updates its sales rankings
on low-selling books each Monday, and that a sale occurred sometime during the week ending September 15, and no sales occurred
during the remaining weeks. On September 30, one of the authors
ordered five copies of this book using five different Amazon user
accounts. The next morning, the book had a sales rank of 4,647.
1588
Table 4
Proportion of Sales from Obscure Titles at Amazon
Sales rank
Proportion in total sales
Standard error∗
>40000
>100000
>250000
47.9%
39.2%
29.3%
2.7%
2.5%
2.0%
Note. ∗ Since the proportion of Amazon unit sales that fall in titles with ranks
above x is a function of 2 and we obtain the standard error of 2 from the
regression, we calculate the standard error of our estimate using the Delta
Method (see Greene 2000, p. 118).
that are not normally stocked by brick-and-mortar
stores. Thus, our rank figure should approximate the
average number of books a consumer could readily
locate in local physical stores.
At one end of the spectrum, one would want to consider consumers who do not have easy access to bookstores with a broad selection of titles. In Appendix C
of Brynjolfsson and Smith (2000), the authors calculated that the average consumer in the United States
lives 5.4 miles away from the closest general selection
bookstore. Using the same data set, we find that 14%
of U.S. consumers live more than 10 miles away from
the nearest general selection bookstore, and 8% live
more than 20 miles from their nearest bookstore. For
such customers, the relevant rank might be near 0.
That is, without the Internet, such customers are not
able to easily purchase even general selection books.
More typically, consumers will have at least one
and possibly multiple bookstores close by. However,
these brick-and-mortar bookstores significantly vary
in size. Small bookshops and mall-based stores stock
approximately 20,000 unique titles, large independent booksellers stock approximately 40,000 unique
titles, Barnes & Noble and Borders superstores stock
approximately 100,000 unique titles, and the Barnes &
Noble superstore in New York City, reported to be the
“World’s Largest Bookstore,” carries 250,000 unique
titles on its shelves.17
In Table 4, we estimate the proportion of total sales
at Amazon.com that lies above a particular rank (i.e.,
17
Stock figures for Barnes & Noble were obtained from correspondence with Mary Ellen Keating, Senior Vice President of Corporate
Communications and Public Affairs, Barnes & Noble, December 3,
2001. Stock figures for independent stores were obtained from multiple industry sources and discussions, including Ritchie (1999).
Management Science/Vol. 49, No. 11, November 2003
BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
titles that are not available at a typical brick-andmortar bookstore) for each of the reference points
discussed above. These calculations are based on
Equation (13) along with the estimate from Table 3
for 2 and 2,300,000 (the number of books in print)
for N . This table shows that 47.9% of Amazon’s unit
sales fall in titles with ranks above 40,000 and 39.2%
of sales fall in titles with ranks above 100,000, as
Figure 2 illustrates. It is unlikely that every consumer
will live within reasonable driving distance to the
largest Barnes & Noble superstore in New York City
and have access to the 250,000 titles stocked there, but
using that number as the cutoff point only reduces
the proportion down to 29.3%.
In subsequent calculations, we use a rank of 100,000
as our point of reference for consumer surplus estimates. This cutoff can be interpreted either in terms
of the average stock levels at a Barnes & Noble or
Borders superstore or as taking into account the possibility that consumers shop at multiple smaller independent stores. For example, if there were only a 50%
overlap in stocked titles at large independent bookstores, a consumer would have to shop at a minimum
of five such stores to have access to 100,000 titles.
Figure 2
This large cutoff point seems fairly conservative on
two dimensions. First, it is unlikely that most consumers, particularly rural consumers as previously
mentioned, have access to this number of unique
titles through local bookstores. Second, even if all consumers had access to these larger stores, the 100,000
cutoff will underestimate true consumer surplus if,
as seems likely, superstores do not stock exactly the
same 100,000 most popular books that Amazon.com
stocks.
4.3. Consumer Welfare
According to Book Industry Trends 2001, book revenue in year 2000 was $24.59 billion (Book Industry
Study Group 2001). Given that the Internet makes up
6% of total book sales (Book Industry Study Group
2001), we estimate that the total Internet book sales
in year 2000 were $1.475 billion. If we assume that
obscure titles account for about the same proportion
of total sales at other Internet book retailers as at
Amazon, the sales of titles that are not available at
a typical brick-and-mortar bookstore are $578 million
based on the estimates in §4.2.
Share of Amazon Sales Above Rank 100,000
8
7
Weekly Sales
6
5
4
3
2
1
0
-
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
2,200
Rank (in thousands)
Management Science/Vol. 49, No. 11, November 2003
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BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
Because these estimates are based on aggregate figures, it is further necessary to ensure that the average prices of obscure books sold on the Internet are
not lower than the average prices of more popular
books sold on the Internet. If this were not true, we
would overestimate the true consumer surplus by
using aggregate figures. To analyze the relative prices
of obscure and more popular books, we selected 100
random books from a sample of all customer searches
at DealTime.com on July 2, 2001. We then categorized the books into obscure and regular titles based
on whether their Amazon.com sales rank was greater
than (obscure) or less than (regular) 100,000.18 Table 5
illustrates that the prices of obscure books are greater
than the prices of regular titles. Thus, if anything, our
estimates using aggregate sales figures will underestimate true consumer surplus from sales of obscure
titles on the Internet.
With these estimates of elasticity and revenue from
obscure book sales, we use Equation (9) to calculate
that the introduction of obscure books in online markets has increased consumer welfare by between $731
million and $1.03 billion in the year 2000 alone, with
standard errors of $46.7 million and $65.8 million,
respectively.19
It is worth noting that our log-linear demand curve
does not restrict consumers’ valuation to be below a
certain dollar amount. One concern, therefore, is that
our consumer surplus estimates could be driven by
a small number of consumers with high valuations.
It might be reasonable to exclude some of these consumers with high valuations on the assumption that
they might have been motivated to find a way to
gain access to the book without using the Internet,
even if that entailed significant personal effort. On
the other hand, they might never have learned of the
book in the first place without the recommendation
18
Analogous results are obtained using a cutoff of 40,000, the number of books stocked at a typical large independent bookseller.
19
Using a cutoff of 250,000 would reduce our consumer surplus
estimates to between $547 and $772 million in the year 2000, with
standard errors of $37.3 million and $52.7 million, respectively.
Using a cutoff of 40,000 would increase our consumer surplus estimates to between $894 and $1.26 billion in the year 2000, with
standard errors of $50.4 million and $71.1 million, respectively.
1590
Table 5
Price Comparison for Obscure and Regular Titles on the
Internet
Amazon sales rank
Average list price
Average Amazon price
Average price at DealTime
Average minimum price at DealTime
Observations
<100000
>100000
$34.53
$29.26
$29.52
$20.03
63
$42.18
$41.60
$39.06
$29.52
37
engines, search tools, and other aids provided by successful online booksellers. Discussions with a publisher suggest that this latter effect is more important
than any substitution away from conventional channels by high-value consumers.
Nonetheless, as a check on the robustness of our
re sults, we can also conduct an analysis in which
we restrict our consumer estimates by excluding
high-value consumers. Excluding all consumers with
valuations above five times a book’s current price
would reduce our current consumer surplus estimates
by 28.0%–40.6%, while excluding consumer valuations above 10 times a book’s current price would
reduce our current consumer surplus estimates by
16.2%–27.5%.
We also calculate the consumer surplus gain
from increased competition and operational efficiency in Internet markets as a point of reference
to the consumer surplus gains estimated above.
Brynjolfsson and Smith (2000) calculated that prices
on the Internet, including shipping and handling
charges, were 6% lower than prices in brick-andmortar retailers due to increased competition and
increased operational efficiency. A fractional price
change of $ will lead to a $∗ change in quantity, according to the definition of price elasticity of
demand. Thus, we have
p x − p0 x0
CV = − 1 1
1+
p1 x1 − 1 + $
p1 1 + $∗ x1
=−
(14)
1+
where CV is the change in consumer surplus, is
the price elasticity, (p1 , x1 are the price and quantity
after the price change, and (p0 , x0 are the price and
quantity before the price change.
Using the same estimates as were used above (i.e.,
p1 x1 = $1475 billion and between −156 and −179),
Management Science/Vol. 49, No. 11, November 2003
BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
Equation (14) shows that the consumer welfare gain
from a 6% drop in price for all titles on the Internet
is between $100.5 million and $103.3 million. Thus,
the consumer welfare gain from the introduction of
obscure books in online markets is between 7.3 (with
a standard error of 0.5) and 10.0 (with a standard error
of 0.6) times as large as the consumer welfare gain
from increased competition and lower prices on the
Internet.
4.4. Discussion
While the magnitude of the consumer welfare gain
from increased variety is large both in absolute terms
and relative to the savings from lower prices, our
approach is imperfect and is likely to underestimate
the total welfare benefits for a number of reasons.
First, it is important to note that the book market
is just one of many markets affected. Online sales of
other consumer product categories, like music CDs,
movies, and electronic products, are likely to also
show significant gains in consumer surplus. Furthermore, gains in all product categories will increase as
more customers gain access to the Internet channel
and as new technologies such as print on demand,
digital content delivery, mobility services, and broadband access further reduce consumer search and
transactions costs. Finally, it is possible that the ability to sell obscure books through Internet channels
that would not have been stocked in physical stores
will allow some books to be published that otherwise
would not have been viable.20
Second, there is some evidence that the Internet
may have reduced the effective cost of special
orders even in offline stores, including the consumer
time and effort required to identify the relevant
books. Some obscure titles were available in brickand-mortar stores through customer initiated special
orders, even before the rise of the Internet as a channel for books. However, according to several bookstore owners we spoke to, special orders for items not
20
While making more and more titles available online will result
in higher sales, it is important to note that our calculations demonstrate that there are diminishing returns to adding titles. For example, according to our Pareto (1896) curve estimates, titles ranked
from 100,000 to 200,000 account for 7.3% of sales at Amazon.com,
while titles ranked from 200,000 to 300,000 account for only 4.6%
of sales.
Management Science/Vol. 49, No. 11, November 2003
normally stocked account for less than 1% of sales
through the physical channel. This low level of special
orders should not be surprising given that the special
order process in a conventional store is inconvenient
and time consuming, as discussed above.
However, it is interesting to note that the availability of obscure titles on the Internet has apparently led to somewhat increased sales through special
orders at brick-and-mortar stores. Several brick-andmortar retailers we spoke to said that the Internet
has allowed brick-and-mortar customers to locate and
evaluate books they would not have been able to find
otherwise. Mary Ellen Keating, Barnes & Noble Senior
Vice President of Corporate Communication and Public Affairs, put it as follows with regard to sales in
Barnes & Noble’s brick-and-mortar stores:
Sales from special orders are up, and customers are
ordering a broader range of titles in a number of different categories. What some customers tend to do is
their own research on the Web and then special order
the book from our stores.21
If the cost of special orders is unaffected by the
Internet, then our consumer surplus calculations can
ignore changes in the quantity of special orders, while
our estimates will be too low if the effective cost of
special orders were reduced as suggested by the preceding quotation.
Lacking precise data on the costs or quantities of
special ordering sales of obscure titles at brick-andmortar stores, this potential consumer welfare gain is
left out in our calculation. However, given that the
Internet has apparently led to a net increase in special
order sales through the physical channel, our calculations will underestimate the true consumer surplus
from the availability of obscure titles on the Internet.
The Internet may also lower the cost of placing special orders in other ways. For example, on October
26, 2000, Barnes & Noble announced a plan to install
Internet service counters in all its superstores. These
service counters would allow in-store consumers to
place orders from Barnes & Noble’s Internet site for
home delivery. While not included in our calculations, the availability of this service will increase consumer surplus by providing Internet access in new
21
Source: Mary Ellen Keating, December 3, 2001, personal e-mail
communication.
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BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
and convenient locations and, thus, lowering the cost
of placing special orders to in-store consumers and
consumers who otherwise would not have access to
the Internet.
Last but not least, our calculations only focus on
consumer welfare gains. There may also be significant
gains in producer welfare from the additional sales.
Indeed, retailers like Amazon, book publishers, printers, and authors all stand to benefit and earn a slice of
the growing pie created by lower search and transactions costs. In contrast, consumer welfare gains from
lower prices come largely at the expense of producers.
This suggests that increased product variety creates a
total welfare gain, including both consumer and producer welfare, which exceeds the total welfare gain
from lower prices by even more than the ratio we estimated for consumer welfare gains alone. It would be
interesting to adapt the methods of this paper to also
explore the implications for producer welfare.
5.
Conclusions
While lower prices due to increased market efficiency
in Internet book markets provide significant benefits
to consumers, we find that the increased online availability of previously hard-to-find products represents
a positive impact on consumer welfare that is seven to
ten times larger. Limited shelf space in conventional
retail outlets constrains the types of products that
can be discovered, evaluated, and easily purchased
by consumers. Limits on the number of titles Internet
retailers can present and sell to consumers are substantially lower. As a result, Internet customers have
easy access to millions of products that they could not
easily locate or purchase through brick-and-mortar
retailers.
To date, the economic effect of increased product variety on the Internet has been ignored, effectively setting to zero the value consumers place on
increased selection at Internet retailers. Recent econometric advances have allowed for the measurement
of the economic impact of such new products. Our
research applies and extends these methodologies to
quantify an important welfare impact of online markets. Preliminary calculations for one product category sold in U.S. markets show that the welfare gains
1592
are between $731 million and $1.03 billion for the year
2000 alone. These welfare gains dwarf the consumer
welfare gain from increased competition and lower
prices uncovered in previous research (Brynjolfsson
and Smith 2000).
There are a variety of ways our results can be
extended by future research. First, while our results
use the well-known Lerner index to obtain price
elasticity estimates, it may be possible for future
research to directly estimate price elasticity using
an experiment in cooperation with a publisher of
obscure books or possibly a retailer with a dominant market position. Such an experiment would
change wholesale prices on a randomly selected set
of titles and track the resulting levels of demand.
These price changes would be exogenous if both the
titles and price change levels (positive and negative)
were selected at random for the purposes of this
experiment.
Second, it would be interesting to analyze whether
consumer surplus gain from access to increased product variety online are primarily from the reduced
transactions cost to order obscure products in Internet markets or from the lower search costs to discover books using Internet search and collaborative
filtering tools. The example mentioned above—that
Barnes & Noble attributes a net increase in special
orders in brick-and-mortar stores to consumers discovering new books online—may shed some light on
this question. If consumers gained more value from
lower transactions costs, we would expect to see a
shift away from in-store special orders to Internet
purchases. The fact that the opposite seems to have
occurred suggests that the value of discovery may
significantly outweigh the value of lower transactions
costs. This question deserves further analysis.
It also should be possible to extend this methodology to measure welfare contributions of other product
categories sold on the Internet or other new products
made available through Internet markets. For example, one could easily extend our results to the online
sale of movies, music CDs, or consumer electronics
products. It also might be possible to estimate consumer surplus gains from the distribution of digital products such as downloadable e-books, music,
movies, and software. Moreover, consumers should
Management Science/Vol. 49, No. 11, November 2003
BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
also benefit from easy access to formerly localized
markets such as RealAudio broadcasts of local radio
stations or eBay auctions for products that would
otherwise have been sold in yard sales. The results of
this paper suggest that, ultimately, the most important
benefits of Internet retailing are not fully reflected in
lower prices, but rather are due to the new goods and
services made readily available to consumers.
Acknowledgments
The authors thank Matthew Gentzkow, Austan Goolsbee, Jerry
Hausman, Lorin Hitt, Guillermo Israilevich, participants at New
York University, University of Arizona, University of California at
Irvine, University of Maryland, University of Pittsburgh, the 2001
Workshop on Information Systems and Economics (WISE), the 2003
American Economic Association Meetings, the 2003 International
Industrial Organization Conference, and the referees and editors of
this journal for valuable comments on this paper. They thank Jeff
Bezos, Al Greco, Vicki Jennings, Mary Ellen Keating, Jason Kilar,
Steve Riggio, Madeline Schnapp, Jeff Wilke, and representatives
of the American Booksellers Association, the American Wholesale
Booksellers Association, the Book Industry Study Group, Barnes &
Noble, Amazon.com, MIT Press and various other publishers and
booksellers for providing valuable information on the book industry. Generous financial support was provided by the Center for
eBusiness at MIT under Grant number IIS-0085725 from Fleet Bank
and the National Science Foundation.
Appendix. Summary of Derivation and Necessary Data
Derivation of the Formula to Calculate Consumer Surplus. (Follows Hausman 1981 and Hausman and Leonard 2002.)
Assume xp y
= Ap y CV = epe0 pn0 u1 − epe1 pn1 u1 xp y
= −
❄
❄
CV = epe1 pn0 u1 − epe1 pn1 u1 + epe0 pn0 u1 − epe1 pn0 u1 Assume pe0 = pe1 = pe
vp y
= −A
❄
CV = epe pn0 u1 − epe pn1 u1 = e pn0 u1 − e pn1 u1 ❙
y 1−
p1+
+
1+ 1−
❄
ep u
= 1 − u +
❙
vp y
/p
vp y
/y
Ap1+
1+
1/1−
❙
✇
❙
CV =
✴
1 − −
y pn0 x0 − pn1 x1 + y 1−
1+
Assume = 0
CV = −
1/1−
−y
Pn0 x0 = 0
❄
Pn1 x1
1+
Notes. If postintroduction prices of existing products are lower than preintroduction prices (i.e., pe0 > pe1 , results under equality assumptions
will underestimate true consumer surplus.
If > 0 (i.e., if the good is a luxury good as opposed to a necessity good), results under = 0 will underestimate true consumer
surplus.
Management Science/Vol. 49, No. 11, November 2003
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BRYNJOLFSSON, HU, AND SMITH
Consumer Surplus in the Digital Economy
Derivation of the Price Elasticity of Aggregate Demand.
Assume pwi = pw
Assume pwi = ki pri
si ≡
qri
qr
Assume qwi = qri
qr ≡
qw ≡
N
i=1
◗
s
◗
pw = ki pri ◗
✸
✑
✑
◗
N
N s
s p̄ ≡ ◗
i
✑
si pri =
pw
i=1
i=1 ki
✸
✑
✑
N
✲ p̄ ≡ s p ✑
i ri
i=1
◗
◗
s
◗
✲ qr = q w
qri
N
i=1
◗
qwi
◗
◗
✑
◗
✸
✑
✑
◗
s
◗
❄
N
N
si
si
pw dqw
pw dqw
p̄dqr
p̄dqw
p dq
i=1 k
i=1 k
= Ni =
= Ni
= w w
si
si
qr d p̄
qw d p̄
qw dpw
qw
qw
dpw
dpw
i=1 ki
i=1 ki
Derivation of the Formula to Calculate Total Sales of Obscure Products on the Internet.
log Quantity
= 1 + 2 · log Rank
+
❄
Estimate of 2
N
rx N = xN
1
❄
2
1 t dt
1 t 2 dt
=
N 2 +1
− x2 +1
N 2 +1
− 1
❄
∗ rx N pn1 x1 = P
Q
1594
Management Science/Vol. 49, No. 11, November 2003
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Consumer Surplus in the Digital Economy
Data Requirements and Potential Sources.
Data requirement
Elasticity of aggregate demand ()
Potential data sources
Lerner index and information on publisher and manufacturer margins.
•
•
•
Total sales of products on the Internet (P
Q
Market research firms.
•
•
Proportion of sales of obscure products at Internet
retailers (rx N •
•
Experimental estimation using power-law relationship between
sales rank and sales and observation of changes in rank after
purchase of goods (Chevalier and Goolsbee 2003, pp. 16–17).
Direct observation from representative publisher and manufacturer.
Direct observation from retailer (or retailers) with dominant market share.
Total sales of product category on the Internet∗ proportion of sales of
obscure products at Internet retailers.
•
•
∗
Department of commerce reports.
Industry consortia.
Estimation using log-linear relationship between sales rank and sales and
data obtained from publisher and manufacturer (pp. 14–16).
•
Total sales of obscure products on the Internet (pn1 x1 Experimental estimation through partnership with publisher and
manufacturer (see pp. 10, 25–26).
Experimental estimation through partnership with
retailer with a dominant market position (see pp. 10, 25–26).
Industry estimates of elasticities.
Direct observation from representative publisher and manufacturer.
Direct observation from retailer (or retailers) with dominant market share.
Potential data sources in bold indicate those used in this paper.
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